Dr Maude David’s laboratory studies gut-brain interactions to understand how the gut microbiota can impact our behavior, specifically in Autism Spectrum Disorder and Generalized Anxiety Disorder. Her team uses a crowd-sourced approach to collect lifestyle information, dietary habits, and microbiome samples. Her laboratory also works on identifying bottlenecks in microbiome data exploration and has been developing new biocomputing methods to improve sequencing data annotation and analysis. Her interest lies in using machine learning algorithms to extract meaningful information from massive datasets already publicly available such as the Human Microbiome Project.
The laboratory has been working on feeding mice specific microbial taxa, which previously were found enriched in individuals in individual with Autism Spectrum Disorder and/or General Anxiety Disorder. Behavior testing has been implemented to evaluate if the microbe being supplemented seems to have an effect on the behavior of the mice. We are also applying a combination of multi-Omics analysis in an attempt to unravel some of the mechanisms underlaying the interactions between the gut microbiota and the brain.
Our laboratory is currently using machine learning to denoise high throughput sequencing datasets, from 16S amplicon data. We're planning on applying this on metagenomic datasets as well, and for this project we're currently using publicly available datasets.
This project aims to understand the role of the microbiome in anxiety. There are already myriad publications connecting gut bacterial taxa with this disorder: Campylobacter jejuni, for example, increases anxiety, while Bifidobacterium longum and Lactobacillus helveticus reduce it. While anxiety mechanisms, especially dysregulation of the hypothalamic–pituitary–adrenal (HPA) axis, have been extensively studied in mice, there is currently a dearth of knowledge of these mechanisms in humans and their association with gut microbiome. The David laboratory is currently developing a crowdsourced study involving undergraduate students from Oregon State University. We will gather phenotypic data including stress level, changes in weight, sleep patterns, and frequency of anxiety attacks, and collect longitudinal microbiome samples during the school year.
Publicly available 16S amplicon datasets, such as the American Gut Project, are enabling meta-analysis of multiple datasets. By carefully combining them, we anticipate to refine the biomarkers involved in anxiety disorder by increasing the power of our analysis while parsing out some of the confounding environmental factors which can also influence the microbial structure of our gastro-intestinal tract.
HMM have been widely used to assign function from potentially distant homologs. This project aims to first develop new models by calibrating the stability of the clusters and alignments generated rather than removing entire sequences from a SEED alignment, as well broaden the use of HMMs by developing a new GUI interface compatible with multiple interfaces.